Bayesian averaging of classical estimates in asymmetric vector autoregressive models

The estimated vector autoregressive (VAR) model is sensitive to model misspecifications, resulting to biased and inconsistent parameter estimates. This article extends the Bayesian averaging of classical estimates, a robustness procedure in cross-section data, to a vector time-series that is estimated using a large number of asymmetric VAR models. The proposed procedure was applied to simulated data from various forms of model misspecifications. The results of the simulation suggest that, under misspecification problems, particularly if an important variable and moving average (MA) terms were omitted, the proposed procedure gives robust results and better forecasts than the automatically selected equal lag-length VAR model.     

Bayesian adaptive dose‐escalation designs for simultaneously estimating the optimal and maximum safe dose based on safety and efficacy

The main purpose of dose‐escalation trials is to identify the dose(s) that is/are safe and efficacious for further investigations in later studies. In this paper, we introduce dose‐escalation designs that incorporate both the dose‐limiting events and dose‐limiting toxicities (DLTs) and indicative responses of efficacy into the procedure. A flexible nonparametric model is used for modelling the continuous efficacy responses while a logistic model is used for the binary DLTs. Escalation decisions are based on the combination of the probabilities of DLTs and expected efficacy through a gain function. On the basis of this setup, we then introduce 2 types of Bayesian adaptive dose‐escalation strategies. The first type of procedures, called “single objective,” aims to identify and recommend a single dose, either the maximum tolerated dose, the highest dose that is considered as safe, or the optimal dose, a safe dose that gives optimum benefit risk. The second type, called “dual objective,” aims to jointly estimate both the maximum tolerated dose and the optimal dose accurately. The recommended doses obtained under these dose‐escalation procedures provide information about the safety and efficacy profile of the novel drug to facilitate later studies. We evaluate different strategies via simulations based on an example constructed from a real trial on patients with type 2 diabetes, and the use of stopping rules is assessed. We find that the nonparametric model estimates the efficacy responses well for different underlying true shapes. The dual‐objective designs give better results in terms of identifying the 2 real target doses compared to the single‐objective designs.     

Sequential estimation problems for negative exponential populations

The literature on sequential estimation problems for negative exponential populations has been reviewed here, We attempt to bring in all the published and unpublished materials known to us in a fairly coherent fashion. Both the concepts and theoretical findings are discussed.     

减刑假释实行申请制之倡导

根据我国刑法规定,减刑假释程序是由执行机关提出减刑假释意见书,法院组成合议庭作出裁定。现有制度存在很大不合理性,犯罪人作为减刑假释的直接获益人,被排斥在程序之外并不能保障犯罪人的利益,而且会导致假释使用率被执行机关人为降低。减刑假释本质都应采用权利说,二者本质上并无差别,且优劣互补,只是具体执行方式不同,因此减刑假释应当实行申请制,由犯罪人根据自身情况自由选择减刑或者假释并先行提出申请,执行机关提出意见书,法院居中裁定。减刑假释实行申请制有利于司法机关选择真正适合犯罪人的执行措施,提高犯罪人改造的积极性,使得对犯罪人的改造更加有效、更加彻底,同时也保障了犯罪人的合法权益。     

Randomization tests in recursive response-adaptive randomization procedures

In a randomized clinical trial, response-adaptive randomization procedures use the information gathered, including the previous patients' responses, to allocate the next patient. In this setting, we consider randomization-based inference. We provide an algorithm to obtain exact p-values for statistical tests that compare two treatments with dichotomous responses. This algorithm can be applied to a family of response adaptive randomization procedures which share the following property: the distribution of the allocation rule depends only on the imbalance between treatments and on the imbalance between successes for treatments 1 and 2 in the previous step. This family includes some outstanding response adaptive randomization procedures. We study a randomization test to contrast the null hypothesis of equivalence of treatments and we show that this test has a similar performance to that of its parametric counterpart. Besides, we study the effect of a covariate in the inferential process. First, we obtain a parametric test, constructed assuming a logit model which relates responses to treatments and covariate levels, and we give conditions that guarantee its asymptotic normality. Finally, we show that the randomization test, which is free of model specification, performs as well as the parametric test that takes the covariate into account.     

货币政策操作工具选择基准的理论分析

中央银行实施货币政策的过程就是运用操作工具实现政策目标的过程，为更好地实现既定的货币政策目标，中央银行应该依据一定的基准选择操作工具。中央银行选择操作工具的基准包括理论基准和实证基准，其中，前者包括外在性基准和内在性基准。内在性基准是其日常操作中最重要的选择基准，它又有主动性基准、微调性基准、信号功能基准、时效性基准和可操作性基准等内容。     

Auditing biochemical research data: A case study

There is little documentation in the literature of how to conduct a data audit of laboratory research. The purpose of such an audit is to verify the extent to which the published data agree with the corresponding laboratory notebook data and whether the data thus examined appear to be sufficient to justify the published conclusions. A case study is presented, which demonstrates a step by step approach towards conducting a data audit of a published research study.     

近代行业纠纷解决机制理论探微——以自贡盐业纠纷解决机制为例

在中国近代的行业纠纷解决机制中,纠纷的当事人依据国家法和行业习惯法的契合,明确了行业纠纷解决中所存在的权利义务关系,并从中找出纠纷解决的合意。行业组织在一定范围之内充当了仲裁者的角色,是行业纠纷解决的主体。在这样的一个过程当中,通过国家的立法,保障了行业纠纷解决机制程序的问题,在一定程度上使行业纠纷解决由原来的注重实质正义向注重程序正义转变,提高了纠纷解决的公信力和权威性。这对当代的行业纠纷解决提供了一个可供参考的借鉴。主要实例来自于四川自贡盐业的档案、文献,盐业纠纷的解决机制实际上在近代的中国呈现出一种解决方式多元化、行业组织与地方法院互相协作、配合和注重习惯法、解纷程序的特点,不失为近代行业纠纷解决机制较为合理的一个蓝本。     

General m-esttmators and applications to bounded influence estimation for non-linear regression

In this paper, we study the M-estimators in the case that λ_F:(β)=E_F:(φ(Z,β))=0 has more than one solution, We show that the numerical iterative procedures converge and that the resulting estimators are consistent and asymptotically normal. We apply them to the non-linear regression models, and then, we find an optimal M-estimate among those that have bounded gross error sensitivity.     

On Calculations Involving the Maximum Cell Frequency

We describe a method of computing the cumulative distribution function of the maximum and minimum cell frequencies in sampling distributions commonly encountered in the analysis of categorical data.The procedure is efficient for exact or approximate calculation in both homogeneous and non-homogeneous cases, is non-recursive, and does not require Dirichlet integrals.Some related statistical problems are also discussed.